Various object recognition modules have been embedded in a variety of electronic devices. For example, a smart TV performs user authentication through face recognition.
Object recognition technologies may cause overhead in the electronic devices because they generally require processes of handling tremendous amount of data. Since the object recognition modules embedded in the individual electronic devices are required to handle the tremendous amount of data in real time in many cases, a technology for handling the data quickly in such individual electronic devices with limited computational capabilities is needed.
Meanwhile, a method using binary descriptors which store information on images of objects in binary codes was suggested. The binary descriptors express information on shape, brightness, appearance, etc. of the objects in binary codes. The method using the binary descriptors guarantee fast and stabilized performance compared to a linear classification method under situations where a lot of training sets are inputted. Binary descriptors include Local Binary Patterns (LBP), Modified Census Transform (MCT), Ferns, etc.
A face recognition method using the LBP may express a value acquired by comparing pixel intensity of a local point with pixel intensities of neighboring pixels as a binary code. Specifically, the face recognition method using the LBP may express a result acquired by comparing the intensity of the center pixel with the intensities of eight neighboring pixels as a binary code.
Similar to the LBP method, even a face recognition method using the MCT may also express a value acquired by comparing pixel intensity of a local point with pixel intensities of neighboring pixels as a binary code. However, the face recognition method using the MCT may express a result acquired by comparing the intensities of nine pixels included in a block of size 3×3 with an average intensity of the nine pixels as a binary code.
However, the LBP method or the MCT method have problems of failing in convergence or failing in pose classification when processes of learning are performed by an object recognition apparatus which includes a module of executing the LBP method or the MCT method. Specifically, there are problems of failing in convergence upon two-class learning process as the case may be (for example, if there are a large degree of variation in training samples) and failing in pose classification upon multi-class learning process.
Contrary to the LBP method and the MCT method, a Ferns method may express a result acquired by comparing an intensity of a first pixel with that of a second pixel which is apart from the first pixel as a binary code. However, since even the Ferns method compares the intensities between the pixels, the Ferns may significantly drop the overall object recognition rates if there occurs any error in a pixel value.
In particular, as an amount of inputted training sets increases, an amount of calculation rises very sharply. Therefore, a new technology for performing the object recognition faster compared to the existing methods, such as HOG, Gabor and the like, is required.